• Coupled systems mechanics
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• v.11 no.2
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• pp.133-149
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• 2022

The accurate prediction of elastoplasticity under prescribed workloads is essential in the optimization of engineering structures. Mechanical experiments are carried out with the goal of obtaining reliable sets of material parameters for a chosen constitutive law via inverse identification. In this work, two sample geometries made of high strength steel plates were evaluated to determine the optimal configuration for the identification of Ludwik's nonlinear isotropic hardening law. Finite element model updating(FEMU) was used to calibrate the material parameters. FEMU computes the parameter changes based on the Hessian matrix, and the sensitivity fields that report changes of computed fields with respect to material parameter changes. A sensitivity analysis was performed to determine the influence of the sample geometry on parameter identifiability. It was concluded that the sample with thinned gauge region with a large curvature radius provided more reliable material parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.932-938
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• 1987

The dynamic equations of robotic manipulators can be derived from either Newton-Euler equation or Lagrangian equation. Model parameters which appear in the resulting dynamic equation are the nonlinear functions of both the inertial parameters and the geometric parameters of robotic manipulators. The identification of the model parameters is important for advanced robot control. In the previous methods for the identification of the model parameters, the geometric parameters are required to be predetermined, or the robotic manipulators are required to follow some special motions. In this paper, we propose an approach to the identification of the model parameters, in which prior knowledge of the geometric parameters is not necessary. We show that the estimation equation for the model parameters can be formulated in an upper block triangular form. Utilizing the special structures, we obtain a simplified least-square estimation algorithm for the model parameter identification. To illustrate the practical use of our method, a 4DOF SCARA robot is examined.

• Journal of the Earthquake Engineering Society of Korea
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• v.6 no.6
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• pp.7-15
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• 2002

In this paper, the nonlinear parameter estimation method using the estimated hysteresis of each structural members was studied for the purpose of efficient seismic damage prediction and estimation of MDOF nonlinear structural model in the shaking table test. The hysteresis of each structural members can be obtained by the conversion of measured response histories into relative motions of each structural members and member forces. These hysteresis can be used to evaluate various kinds of damage indices of each structural members. The MDOF nonlinear structural model for further analysis(re-analysis) can be easily reconstructed using estimated nonlinear structural parameters of each structural members. To demonstrate the proposed techniques, several numerical and experimental example analyses are carried out. The results indicate that the proposed method can be very useful to assess local seismic damages of structures.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.470-470
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• 2000

This paper describes a new method of identifcation of backlash nonlinear systems by use of M-sequence correlation method. In this method, we can obtain not only Volterra kernels of up to 3rd order of the nonlinear system, but also the width of the backlash element from observing the crosscorrelation between the input and the output. Here strictly speaking, a multi-valued nonlinear system such as backlash element can not be expressed by Volterra kernel representation mathematically. But in practice, we encounter many cases where it is difficult to treat them mathematically but they can be controlled from experience. So we here dare to suppose that backlash nonlinear system can be approximated by Volterra kernel representation. Simulations are carried out on a nonlinear system consisting of linear part plus backlash element. And Volterra kernels are measured. The output calculated from the observed Volterra kernels is in good agreement wi th the actual output. And we show that we can obtain the width of backlash element, which is one of the most important parameters, by observing the maximum value of crosscorrelation function between the input M-sequence and the output.

• Structural Engineering and Mechanics
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• v.41 no.1
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• pp.25-41
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• 2012

Kriging surrogate model provides explicit functions to represent the relationships between the inputs and outputs of a linear or nonlinear system, which is a desirable advantage for response estimation and parameter identification in structural design and model updating problem. However, little research has been carried out in applying Kriging model to crack identification. In this work, a scheme for crack identification based on a Kriging surrogate model is proposed. A modified rectangular grid (MRG) is introduced to move some sample points lying on the boundary into the internal design region, which will provide more useful information for the construction of Kriging model. The initial Kriging model is then constructed by samples of varying crack parameters (locations and sizes) and their corresponding modal frequencies. For identifying crack parameters, a robust stochastic particle swarm optimization (SPSO) algorithm is used to find the global optimal solution beyond the constructed Kriging model. To improve the accuracy of surrogate model, the finite element (FE) analysis soft ANSYS is employed to deal with the re-meshing problem during surrogate model updating. Specially, a simple method for crack number identification is proposed by finding the maximum probability factor. Finally, numerical simulations and experimental research are performed to assess the effectiveness and noise immunity of this proposed scheme.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.99-108
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• 1998

In this paper, we proposed a multi-layer neural network for direct control method of nonlinear system. The proposed control method uses neural network as the controller to learn inverse model of plant. The neural network used consists of two parts; one part is for identification of linear part, and the other is for identification of nonlinear part of inverse system. The neural network has to be learned the liner part with RLS algorithm and the nonlinear part with error of plant. From the simulation and experiment of tracking control to use one link manipulator as plant, we proved usefulness of the proposed control method to comparing to conventional direct neural network control method. By comparing the two methods, from simulation and experiment, we were convinced that the proposed control method is more simple and accuracy than the conventional method. Moreover, number of weight and bias to be controller parameter are small, and it has smaller steady state error than conventional method.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.8
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• pp.78-86
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• 2000

This study identified a linear time-invariant mathematical model of each stand of a five-stand tandem cold mill. Two model identification methods are applied to construct a linear model of each stand of the tandem cold mill. For the model identification the input-output data that have interstand interference property in tandem cold rolling are obtained from a nonlinear simulator of the tandem cold mill. And a linear model of each stand is identified with N4SD(numerical algorithms for subspace state space system identification) method based on a state-space model and Least Square algorithm based on a transfer function. Furthermore a modeling error of the tandem cold mill is quantitatively analyzed from a maximum singular value plot of error function between an identified nominal model and uncertain model. In conclusion the comparison of the output signals between the existing Taylor linearized model the identified linear model and the nonlinear model of the tandem cold mill shows the accuracy and the applicability of the proposed identified model.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.3
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• pp.183-190
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• 2016

In this study, we conduct linear and nonlinear modeling of the DC motor driving system of a wheeled mobile robot, which is a nonlinear system involving dead zone, friction, and saturation. The DC motor driving system consists of a DC motor, a wheel, and gears. A linear DC motor driving system is modeled using a steady-state response and parameter measurements. A nonlinear DC motor driving model is identified with the use of the Hammerstein-Wiener method. By using these models, PID controllers for the DC motor system are then established. Each PID controller is applied as a low-level controller in order to achieve posture stabilization control for the real mobile robot. We also compare the performance of the proposed PID controllers in posture stabilization experiments by using several different final robot postures.

• Smart Structures and Systems
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• v.3 no.3
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• pp.245-261
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• 2007

Nonlinear time-domain system identification (SI) algorithm is proposed to assess damage in a shear building by synchronously estimating time-varying stiffness and damping parameters using measured acceleration data. Mass properties have been assumed as the a priori known information. Viscous damping was utilized for the current research. To chase possible nonlinear dynamic behavior under severe vibration, an incremental governing equation of vibrational motion has been utilized. Stiffness and damping parameters are estimated at each time step by minimizing the response error between measured and computed acceleration increments at the measured degrees-of-freedom. To solve a nonlinear constrained optimization problem for optimal structural parameters, sensitivities of acceleration increment were formulated with respect to stiffness and damping parameters, respectively. Incremental state vectors of vibrational motion were computed numerically by Newmark-${\beta}$ method. No model is pre-defined in the proposed algorithm for recovering the nonlinear response. A time-window scheme together with Monte Carlo iterations was utilized to estimate parameters with noise polluted sparse measured acceleration. A moving average scheme was applied to estimate the time-varying trend of structural parameters in all the examples. To examine the proposed SI algorithm, simulation studies were carried out intensively with sample shear buildings under earthquake excitations. In addition, the algorithm was applied to assess damage with laboratory test data obtained from free vibration on a three-story shear building model.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.2
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• pp.53-59
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• 2006

Dynamic neural networks have been applied to diverse fields requiring temporal signal processing such as system identification and signal prediction. This paper proposes the gamma neural network(GAM), which uses gamma memory kernel in the hidden layer of feedforward multilayered network, to improve dynamics of networks and then describes nonlinear adaptive prediction using the proposed network as an adaptive filter. The proposed network is evaluated in nonlinear signal prediction and compared with feedforword(FNN) and recurrent neural networks(RNN) for the relative comparison of prediction performance. Simulation results show that the GAM network performs better with respect to the convergence speed and prediction accuracy, indicating that it can be a more effective prediction model than conventional multilayered networks in nonlinear prediction for nonstationary signals.